Traffic can be a major problem in some areas, such as in some urban infrastructures. In particular, the capacity of some road networks can be at its limits, and frequent traffic jams or other congestions can impact economic productivity and other factors. As a result of increased urbanization, population density, motorization, and general population, traffic congestion has been increasing on transport infrastructures. In some of these areas, the ability to construct more roads can be untenable or impossible. Therefore, the efficient vehicular transport of people and goods is vital to economies.
There is a need to accurately and realistically predict traffic flow patterns within traffic infrastructures and networks. Further, because many urban areas are experiencing population growth, the need is expanding. Various vehicle following models are presently used to model traffic flows and patterns. For example, existing modeling algorithms include Chandler Model, Generalized GM Model, Gipps Model, Krauss Model, Leutzbach Model, Cellular Automata, Optimum Velocity Model, Newell Model, and others.
Some of the existing vehicle following modeling algorithms are dependent on a history of data. For example, when a vehicle switches lanes, history information can be “lost” and the results of some of the existing models can be inaccurate or incomplete. In contrast, the Cellular Automata (CA) model can be a useful model because it depends only on the previous step of the model. However, the CA model can sometimes prove, in some situations, to be inaccurate or otherwise insufficient.
Therefore, it may be desirable to have systems and methods for improving the performance and accuracy of traffic models. In particular, it may be desirable to have systems and methods for modifying the CA modeling algorithm to increase the accuracy and efficiency of traffic simulators.